204results about "Forest fire alarms" patented technology

The invention discloses an intelligent forest fire monitoring method and device, electronic equipment and a medium, and the monitoring method employs a multi-mode deep learning model based on a double-attention mechanism to enhance the capturing capability of early flame thermal radiation characteristics and smoke form characteristics, and greatly improves the recognition sensitivity. In combination with triple verification and a confidence coefficient decision-making mechanism, errors caused by environmental interference are effectively avoided, and the recognition reliability is comprehensively improved; meanwhile, based on intelligent gridding three-dimensional monitoring of terrain complexity and vegetation types, the blind area coverage rate is greatly reduced, normalized inspection and post-disaster quick response are performed through the unmanned aerial vehicle platform, the monitoring coverage range is enlarged, and the response efficiency is improved.

The invention discloses a fire risk accurate prediction method and system based on a multi-source data fusion knowledge graph, and belongs to the technical field of machine learning, and the method comprises the following steps: accessing a multi-source heterogeneous data set, integrating sensor data, geographic information, historical data and external data, and obtaining a multi-source heterogeneous data set; a full-size forest three-dimensional model is constructed by means of FDS and SolidWorks, combustion simulation is carried out, environmental parameters are set carefully to ensure that the result is accurate, and a foundation is laid for construction of a multi-source heterogeneous data set; the method comprises the steps of data acquisition, data preprocessing, noise data cleaning, space-time alignment and feature engineering, multi-source heterogeneous data acquisition and preprocessing are carried out, and forest fire related data covering structured, semi-structured and non-structured types are acquired from a multi-source heterogeneous data set. The accuracy and timeliness of fire early warning are remarkably improved, effective fusion of forest fire multi-source heterogeneous data can be achieved, and transparency and traceability in the data fusion process are ensured.

The invention discloses a forest fire dynamic monitoring method and system based on multi-source data fusion, and relates to the technical field of fire dynamic monitoring, and the method comprises the steps: carrying out the calculation of the fire occurrence probability of a forest region, and outputting a dynamic risk thermodynamic diagram; non-uniform deployment of integrated sensing units is carried out to form a ground fire sensing network; large-range abnormal hot spot distribution is obtained; performing abnormal hot spot cooperative verification, and outputting a real-time fire risk alarm; matching a high-credibility unmanned aerial vehicle according to the fire risk alarm to execute real-time fire image acquisition; fire spreading modeling is carried out in combination with real-time meteorological data, and a fire spreading trend prediction result is obtained; and executing high-response fire extinguishing management and control of the forest area. According to the invention, the technical problem of low fire extinguishing management and control efficiency caused by inaccurate forest fire monitoring risk in the prior art is solved, and the technical effects of realizing dynamic monitoring and efficient management and control of forest fire and improving the efficiency and timeliness of fire extinguishing management and control are achieved.

The invention relates to the field of power transmission line forest fire monitoring, and discloses a power transmission line multi-mode fusion intelligent forest fire monitoring method which is used for constructing an all-dimensional power transmission line forest fire prevention monitoring system and remarkably improving the intelligent level of power transmission line forest fire risk prevention and control. Comprising the steps of reasoning and identifying a smoke and fire target for a fused feature set by utilizing an improved YOLOv8 algorithm model, realizing accurate positioning of a fire point by adopting a haze penetration thermal imaging technology and a smoke cooperative monitoring technology in combination with spatial positioning, and establishing a temperature and humidity and forest fire occurrence probability correlation model based on historical data. According to the real-time temperature and humidity parameter dynamic adjustment algorithm model, the confidence threshold and the sensor acquisition frequency are detected, a dynamic threshold control method is adopted, an early warning threshold is adaptively adjusted according to parameters such as environment illumination, a final early warning result is output after cross validation of a multi-modal data fusion strategy, and the safety of the power transmission line is effectively guaranteed.

The invention belongs to the technical field of safety early warning, and discloses a forest fire suppression remote command system based on an artificial intelligence meteorological big model and a digital twinborn technology. The laser radar monitoring module collects fire scene basic information and transmits the fire scene basic information to the artificial intelligence meteorological large model prediction module for trend prediction. A prediction result and monitoring data are fused through the digital twinborn situation display module and then fire scene situation information is displayed. A commander makes a decision based on the three-dimensional visual fire scene environment, the command decision and emergency response module generates a hierarchical response and combat scheme, and the personnel, material and air-ground collaborative scheduling module executes specific tasks according to the hierarchical response and combat scheme. The method has the advantages that the fire scene information can be acquired in real time and completely; the prediction capability is obviously improved and dynamically updated; multi-source data deep fusion and digital twinning dynamic display are realized; a complete hierarchical response and command closed loop is formed; and cross-region and cross-department resource scheduling is more efficient.

The invention provides a forest disaster detection method and device, and belongs to the field of forest disaster monitoring, and the method comprises the steps: collecting an infrared image, a stereoscopic vision image and positioning data of a target forest region in real time; an ASSA module is introduced in the feature extraction stage of the YOLOv11, and an infrared image is input into the improved YOLOv11 for flame target recognition; recognizing the stereoscopic vision image through a smoke and fire recognition algorithm, and detecting flame and smoke targets; determining a fire detection result by combining the identification results of the flame target and the smoke target; under the condition that the fire disaster is determined to occur, an HITNet algorithm is adopted to carry out ignition point positioning through a stereoscopic vision image, and the geographic coordinates of a fire disaster point are determined based on positioning data; and sending alarm information based on the fire point geographic coordinates. Therefore, the capability of extracting the flame heat radiation characteristics of the tiny fire points is effectively enhanced, and the detection result of the forest fire is improved.

The invention discloses a power transmission line forest fire early warning method based on multi-modal image fusion, and aims to solve the problems that when an existing forest fire early warning method is in butt joint with the actual operation condition of a power transmission line, multi-source information complementation is difficult to consider at the same time, false detection and missing detection are often caused by environmental influence, and the early warning timeliness sometimes cannot meet the real-time requirement of power grid operation. According to the method, a power transmission line forest fire early warning model based on multi-modal image fusion is constructed, and the model comprises preprocessing, multi-scale feature extraction, fire point detection and positioning, risk level definition and early warning level division. And training the model according to the visible light image, the infrared image and the radar image at each moment in the process from the beginning to the end of the mountain fire combustion. The invention belongs to the technical field of power transmission line forest fire early warning.

The invention provides a lightning trajectory data fusion detection based single-tree accurate early warning system for lightning fire, and relates to the technical field of meteorological disaster monitoring and early warning and forest lightning fire prevention and control. The lightning trajectory data fusion detection based single-tree accurate early warning system comprises an electromagnetic radiation receiving module which adopts an antenna array composed of at least four directional antennas to receive electromagnetic radiation signals generated by lightning, the antenna array is connected with a signal conditioning circuit, and the signal conditioning circuit is connected with the electromagnetic radiation receiving module. The signal conditioning circuit amplifies the received weak electromagnetic signal by 100-1000 times, processes the weak electromagnetic signal with a filtering bandwidth of 10 kHz to 1 MHz, and transmits the weak electromagnetic signal to a data acquisition card with a sampling frequency of 5-20 MS / s and a sampling precision of 12-16 bits. By integrating lightning electromagnetic radiation continuous sampling, infrared, visible light and ultraviolet multispectral tracking shooting and atmospheric electric field early warning multi-modal data, a lightning track is accurately detected, and by combining forest environment information, accurate early warning of a single tree where a lightning fire may occur is achieved.

The invention relates to the technical field of disaster prevention and reduction of power systems, in particular to a power transmission line forest fire spreading range prediction method and system based on multi-factor fusion, and the method comprises the steps: collecting multi-source data in real time, and building a fusion database through time-space alignment processing; extracting fire scene features by adopting an image processing technology, and calculating a live line distance and vegetation combustion parameters by combining laser radar data; constructing a prediction model, and dynamically correcting fire spreading parameters; and simulating a spreading path based on the three-dimensional model and topographic data, evaluating the influence degree of the fire behavior on the power transmission line, and generating an early warning strategy. The problems of smog shielding monitoring failure, insufficient night prediction precision and the like caused by dependence of a single data source are effectively solved, the forest fire early warning capability in a complex environment is remarkably improved, and high-precision and real-time technical support is provided for disaster prevention and reduction of a power system.

The invention discloses a fire behavior identification method, system and device suitable for power grid inspection and a medium, and belongs to the technical field of mountain fire monitoring, and the method comprises the steps: obtaining multi-mode video data of a to-be-monitored region, and obtaining a synchronized multi-mode video frame according to the multi-mode video data; determining a baseline model of preliminary training convergence based on the synchronized multi-modal video frame and a preset forest fire recognition model; obtaining an optimized baseline model by obtaining unlabeled video data and combining the baseline model; and performing fire behavior identification based on the optimized baseline model and the synchronized multi-mode video frame to obtain a fire behavior identification result. The method has the beneficial effects that the accuracy and reliability of fire identification are improved, continuous and stable detection under the actual background condition across day and night is realized, and accurate and timely fire early warning guarantee is provided for power grid inspection and forest fire prevention.

The invention discloses a power grid facility safety-oriented forest fire spreading early warning method and system, and particularly relates to the field of cloud edge collaboration and danger prediction.The method comprises the steps of arranging a plurality of monitoring nodes in a power grid protection area, collecting monitoring data of temperature, humidity, wind speed, smoke concentration and spectral radiation, and performing early warning on forest fire spreading; matching the monitoring data of each monitoring node with the corresponding acquisition time, and constructing a multi-node environment data set according to a matching result; according to the method, a time rhythm index and multi-node difference identification mechanism is established under a cloud edge collaborative architecture, and an integrated danger prediction process of data synchronization, difference correction and trend solution is realized, so that the problems of virtual height early warning and response lag caused by misjudgment of node data asynchronization and cloud fusion in existing forest fire monitoring are solved.

The invention relates to the technical field of forest fire prevention monitoring, in particular to a forest fire prevention monitoring method and system based on multi-modal data and intelligent collaboration, and the method comprises the steps: obtaining thermal infrared images of a forest region at all moments, and the temperatures and wind speeds of different monitoring points in the forest region at all moments; calculating the temperature anomaly degree of each pixel point, and extracting all abnormal areas; determining a distribution characteristic value, a heat source evaluation value and a suspected degree of the abnormal region, and screening out a suspected region; acquiring a matching point corresponding to each pixel point in each frame of grayscale image; calculating the change confusion degree and the flicker evaluation value of each pixel point, and screening flicker points; and determining a flame edge value of each flicker point, and extracting a flame region in each frame of grayscale image. According to the method, the accuracy and reliability of forest fire identification are improved, and meanwhile, the false alarm rate caused by complex environment interference is remarkably reduced.

The invention provides an unmanned aerial vehicle forest fire prevention smoke intelligent early warning method, device and equipment and a medium, and the method comprises the steps: detecting a smoke concentration value in a target inspection region at a current moment, when the smoke concentration value is greater than a preset concentration value, acquiring a multi-frame image sequence in a first preset time period and acquiring a multi-frame image sequence in a second preset time period; smog feature analysis is carried out on the multi-frame image sequence in the first preset time period and the multi-frame image sequence in the second preset time period to obtain smog multi-dimensional features; a thermal imaging data sequence in a second preset time period is collected, and flame multi-dimensional features are extracted; based on the smog multi-dimensional features and the flame multi-dimensional features, a fireproof early warning grade is judged; if the fireproof early warning level is greater than the preset early warning level, fusing the fire source navigation positioning information and the fire source dead reckoning information to obtain a fire source position coordinate; and generating a fireproof early warning prompt based on the fire source position coordinates, and sending the fireproof early warning prompt to a ground control center. Therefore, the forest fire prevention efficiency is effectively improved.

The invention discloses a forest fire rapid identification and accurate fire point positioning method based on air-ground cooperation. Comprising the following steps: a forest fire panoramic monitoring and positioning method based on a double-axis holder, a wildfire target intelligent identification algorithm based on multi-modal image fusion analysis, and a low-power-consumption air-ground cooperative communication network oriented to forest fire monitoring. The invention relates to an unmanned aerial vehicle-based forest wildfire target maneuvering inspection and positioning method and an air-ground fusion wildfire target accurate positioning method. According to the invention, 360-degree panoramic image acquisition is realized by using the double-shaft holder, and meanwhile, a fire source position self-adaptive adjustment mechanism is provided; multi-modal image fusion analysis is adopted in the image processing layer to resist interference, blind areas are compensated, and the fire source recognition accuracy is improved; a low-power-consumption air-ground cooperative communication network is constructed, so that the cost is reduced, and flight is continued; and the monitoring limitation is broken through by means of flexible inspection of the unmanned aerial vehicle, a closed-loop system of air-ground cooperation-information complementation-intelligent verification is formed, and the monitoring stability in a complex scene is remarkably enhanced.

The invention discloses a forest fire intelligent monitoring and early warning method and system based on a multi-modal large model, and the method comprises the steps: carrying out the fire recognition and environment analysis based on a multi-modal data fusion mechanism of the multi-modal large model in combination with multi-source data collected by an unmanned plane; an auxiliary early warning solution is automatically generated for the fire by combining a multi-modal model with a forest ranger inspection report, an unmanned aerial vehicle inspection video and a fire analysis result, and emergency processing suggestions are provided; massive multi-source heterogeneous data in the forest fire intelligent monitoring and early warning system is efficiently stored, managed and retrieved, the distributed training and reasoning process of a multi-modal large model is optimized, and it is ensured that the system can achieve rapid and accurate fire monitoring and early warning in a complex natural environment. According to the invention, full-process automatic management and comprehensive analysis from data acquisition, fire monitoring to analysis and decision making are realized, and problems of insufficient intelligent identification capability, high false alarm rate and low data integration analysis efficiency in the existing forest fire monitoring and early warning process are solved.

The invention discloses a forest fire unmanned aerial vehicle video monitoring system and method. The system comprises a data acquisition unit arranged on an unmanned aerial vehicle and responsible for acquiring multi-source monitoring data and spatial attitude data of a target area; the data processing unit is arranged on a ground station and is used for receiving and processing the data and generating fused situation data containing the position and situation of the fire behavior through a fire behavior recognition algorithm; the dynamic path planning unit is used for automatically generating a cruising or path tracking instruction according to the fused situation data, the geographic information and the state of the unmanned aerial vehicle; and the command decision-making unit is arranged in a ground command center and is responsible for visualized display, spatial analysis and emergency command of the fire behavior and issuing a path instruction to the unmanned aerial vehicle for execution so as to realize a monitoring and disposal closed loop. According to the invention, multi-source data efficient processing, fire accurate identification, dynamic path planning and emergency command efficient linkage can be realized, the monitoring coverage range and the response speed are improved, the labor cost is reduced, and the fire loss is reduced.

The invention discloses a forest fire automatic identification and risk assessment method based on X-band and S-band dual-band joint detection, and the method comprises the steps: deploying an X-band radar and an S-band radar to construct a cooperative detection network, and forming a detection region covering short and long ranges through space-time registration; a Kalman filtering algorithm is adopted to fuse dual-source radar data, and the method comprises the following steps: defining a four-dimensional state vector containing a plane position coordinate and a velocity component; directly mapping the position component of the state vector to a radar reflectivity plane observation value by using a position mapping type observation matrix; fire point identification based on a random forest model comprises the following steps: inputting feature fusion space reflectivity parameters and time dynamic features; calculating the heat release rate per unit area through the linear combination of the dual-polarization radar reflectivity and the differential reflectivity; calculating fire intensity by combining the fire point area; and coupling the fire intensity with the wind field spreading prediction, and generating graded early warning information oriented to power grid safety.

Systems and methods for utility infrastructure condition monitoring, detection, and response are disclosed. One exemplary system includes a sensor package and a monitoring and control module. The sensor package includes a plurality of sensors such as, for example, an image sensor, a video sensor, and a LiDAR sensor. The sensors may each be configured to capture data indicative of one or more conditions (e.g., an environmental condition, a structural condition, etc.) in the vicinity of the utility infrastructure. The monitoring and control includes a detection module and an alert module. The detection module is configured to receive data captured by each sensor and, based on the captured data, determine one or more conditions in the vicinity of the utility infrastructure. The detection module may be configured to then, based on the determined conditions, provide an alert for the condition using the alert module.

The invention discloses a forest fire prediction method, which comprises the following steps of: 1, dividing a forest region into a plurality of sub-regions, and generating a digital map; step 2, collecting fire factors of each sub-region, wherein the fire factors comprise static fire factors and dynamic fire factors; 3, carrying out risk grading based on the fire factors, and outputting region classification information; 4, deploying a monitoring network according to the region classification information and acquiring monitoring data; 5, processing the monitoring data through a graph neural network model, and outputting a fire danger probability; and 6, when the fire danger probability exceeds a threshold value, starting an emergency response mechanism. According to the invention, by fusing static and dynamic fire factors, combining with meteorological data to dynamically correct risk classification and differentially deploy monitoring resources, the prediction precision is optimized by adopting a dual-channel graph neural network model, and a closed-loop mechanism from monitoring to prediction to emergency is established, so that the accuracy of forest fire prediction and the emergency disposal capability are improved.

The invention relates to the technical field of forest fire alarm monitoring, in particular to a forest fire early warning method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring image data and atmospheric spectrum data of a forest area; the atmospheric spectral data is spectral data which is acquired through a Fourier transform infrared spectroscopy FTIR technology and contains a COcharacteristic absorption wave band; identifying visual features of flames and / or smoke from the collected image data through a deep learning model; carrying out baseline calibration and characteristic peak identification processing on the atmospheric spectrum data, and calculating the carbon dioxide concentration; determining a fire danger grade according to the visual features and the carbon dioxide concentration; and generating early warning information based on the fire danger level. Compared with the prior art, the FTIR technology is introduced into the field of forest fire early warning, so that the monitoring precision and the response rate are greatly improved. Meanwhile, early warning evaluation is carried out by fusing two dimensions of optical images and carbon detection instead of being based on a single index, so that the monitoring precision is improved, and the false alarm rate is reduced.

The invention provides a sky-ground multi-network integration ubiquitous comprehensive sensing intelligent forest fire early warning and spreading behavior prediction system. The sky-ground multi-network integration ubiquitous comprehensive sensing intelligent forest fire early warning and spreading behavior prediction system comprises a multi-dimensional detection sensing terminal, a multi-stage wireless sensing gateway, a sky-ground integrated multi-network integration network, a ground base station, a networked data management system and a mobile phone end warning situation sensing app. The multi-dimensional detection sensing terminal is used for acquiring data of temperature and humidity, smoke and atmospheric pressure of a forest, the multi-stage wireless sensing gateway is used for transmitting the data acquired by the detection sensing assembly, and the unmanned aerial vehicle has patrol and sensing capabilities and can sense environmental data of the forest and send the environmental data to the background server; the multi-dimensional detection sensing terminal obtains forest related data, the forest related data is transmitted to the Internet of Things satellite through the multi-stage wireless sensing gateway, the Internet of Things satellite transmits the forest related data to the ground base station and the data management system, and the data management system receives and processes a data packet and sends the processed data to the mobile phone terminal alarm situation app.

The invention discloses a forest fire early recognition and early warning system and method based on multi-source sensing and artificial intelligence, and belongs to the technical field of forest fire intelligent monitoring. The system comprises a gridding multi-source sensing deployment module, a multi-modal time sequence data collaborative acquisition module, a cross-modal feature coupling and state diagnosis module, a multi-source heterogeneous feature adaptive fusion and fire behavior identification module and an intelligent grading early warning and dynamic resource collaborative scheduling module. The method comprises the steps of heterogeneous grid division and sensing deployment, multi-modal time series data collaborative acquisition, cross-modal feature coupling analysis, multi-channel attention fusion recognition, and graded early warning and dynamic scheduling. Through multi-source data collaborative sensing, cross-modal feature coupling analysis, adaptive fusion recognition and intelligent scheduling response, accurate and rapid intelligent early warning of the early stage of the forest fire is realized, and the intelligent level and emergency response efficiency of forest fire prevention and control are significantly improved.

Systems and methods for utility infrastructure condition monitoring, detection, and response are disclosed. One exemplary system includes a sensor package and a monitoring and control module. The sensor package includes a plurality of sensors such as, for example, an image sensor, a video sensor, and a LiDAR sensor. The sensors may each be configured to capture data indicative of one or more conditions (e.g., an environmental condition, a structural condition, etc.) in the vicinity of the utility infrastructure. The monitoring and control includes a detection module and an alert module. The detection module is configured to receive data captured by each sensor and, based on the captured data, determine one or more conditions in the vicinity of the utility infrastructure. The detection module may be configured to then, based on the determined conditions, provide an alert for the condition using the alert module.

The invention discloses a forest fire danger early warning system and method based on meteorological and vegetation data analysis, and relates to the technical field of fire risk prediction and control. According to the forest fire danger early warning system and method based on meteorological and vegetation data analysis, fine grid division is carried out on a target forest area through a high-resolution DEM and a multispectral or hyperspectral image, and vegetation type identification is carried out on each monitoring subunit; calculating a wet-dry cycle index WDI, a monsoon effect index MFI and a dry thunderstorm risk factor DRF by combining hourly wind speed, wind direction, relative humidity, precipitation and vapor pressure difference meteorological data; through extracting canopy height, canopy density, under-forest shrub and grass layer thickness and high VOC tree species coverage rate, fuel continuity coefficient FCI and volatile matter flammability index VEI are calculated, and accurate quantification of continuity and flammability of vertical ladder fuel and horizontal canopy is realized. And a comprehensive fire danger index FFI is constructed and subjected to hierarchical management after judgment, so that the dynamism, the hierarchical property and the pertinence of fire danger early warning are realized, and the forest fire danger prevention and control precision and the patrol resource utilization efficiency are improved.

The invention discloses a forest fire risk dynamic assessment and early warning system based on multi-source data fusion and artificial intelligence, and belongs to the technical field of forest fire prevention and disaster early warning. The system aims at solving the technical problems that in an existing forest fire monitoring technology, evaluation lags behind, data is single, and risk prospective prediction cannot be achieved. According to the technical scheme, satellite microwave remote sensing, optical remote sensing and near real-time meteorological and topographic data are integrated through a multi-source data acquisition module, and are fused and processed through a feature extraction module to generate a fire danger influence factor feature set containing the water content, the load capacity, the meteorological conditions and the topographic features of combustible materials; a dynamic risk distribution diagram in a future preset time period is output through a machine learning model of the dynamic risk assessment module, and finally automatic early warning is achieved through the early warning and decision making module. The system is mainly used for realizing scientific and accurate assessment and early warning of forest fire risks, and provides technical support for forest fire prevention decision making.

The invention is applied to the technical field of forest fire prevention, and particularly discloses a forest fire prevention water storage monitoring system based on environment temperature calculation, the forest fire prevention water storage monitoring system comprises a central computer, a data monitoring module, a data processing and analyzing module, a risk assessment module and an early warning output module, and the central computer is a processing center of the system. According to the forest fire prevention and water storage monitoring system based on environment temperature calculation, environment data, including multi-dimensional environment data such as environment temperature, humidity, wind speed and wind direction, in a monitoring area is collected through a data monitoring module, a fire risk assessment model is constructed on the basis of a decision-making tree, and the forest fire prevention and water storage monitoring system based on environment temperature calculation is established in combination with historical fire data of the area. According to the method, the fire risk assessment model is trained and corrected, so that the risk of the forest fire can be predicted through the environmental data in the monitoring area, and compared with a traditional mode of monitoring the forest fire through visual monitoring, the forest fire can be predicted in advance through the environmental data.

The invention belongs to the technical field of meteorological safety, and discloses a forest fire spreading automatic forecasting method integrating laser radar detection and micro-meteorological judgment. According to the method, high-temporal-spatial-resolution real-time monitoring of forest fire points is realized through a laser radar system, and micro-meteorological elements such as particle concentration, wind speed and wind direction around the fire points are inverted based on radar echo data, so that a real-time micro-meteorological judgment mechanism is formed. A judgment result automatically drives WRF-SFIRE numerical fire spreading simulation, and automatic prediction and dynamic updating of a fire spreading trend are carried out in combination with high-resolution numerical weather forecast data. A closed-loop intelligent forecasting process of fire point detection-microclimate judgment-automatic triggering simulation-trend forecasting output is integrally constructed, and the early-stage finding and spreading trend forecasting capability of the forest fire is remarkably improved.

The invention belongs to the field of satellite remote sensing fire monitoring, and discloses a satellite remote sensing forest fire initial fire point monitoring method, which comprises the following steps: acquiring satellite data as input data; reading the zenith angle of the sun in the satellite data, and judging the time period as a day time period, a night time period and a morning and night junction time period; the solar zenith angle, the sensor zenith angle and the like need to be read in daytime period data so as to filter solar flares; carrying out simple cloud detection on data in all time periods by utilizing 11-micron channel brightness temperature and 11-4-micron channel brightness temperature, expanding an adaptive window by taking a suspected initial fire point pixel as a center, and calculating 4-micron channel brightness temperature difference, 11-micron channel brightness temperature difference and 4-11-micron channel brightness temperature difference of a background pixel by utilizing a time sequence method; and counting the mean value and the standard deviation of the background pixels, and confirming and extracting a forecast initial fire point by using a normal distribution starting point algorithm. According to the method, the timeliness and accuracy of early warning of the fire disaster are improved, and important technical support is provided for forest fire prevention decision making.

The present application belongs to the technical field of forest fire research, and specifically discloses a forest fire simulation monitoring method and system. The method divides multiple forest fire simulation areas, determines the area where the ignition point is located as a target area; uses a UAV to carry a Fourier infrared spectrometer to collect spectral images in the target area; compares the spectral images with the spectral bands generated by the combustion of existing combustion products to determine the types of combustion products in the target area; based on the spectral images in the target area and the corresponding combustion product types, a flame spectral radiation model is constructed; multiple forest fire danger levels and the corresponding flame spectral radiation ranges of each forest fire danger level are set in advance, and the flame spectral radiation parameters output by the flame spectral radiation model are compared with all flame spectral radiation ranges to determine the forest fire danger level of the target area. By using the flame spectral radiation model, the present application realizes accurate detection and identification of forest fires.

The application discloses a power transmission line fire alarm method and system based on multi-source data fusion, the method comprising: monitoring fire points according to ground monitoring device monitoring data, unmanned aerial vehicle monitoring data and satellite monitoring data of a target area where the power transmission line is located; when it is judged that there is a fire point according to the satellite monitoring data and multiple satellites monitor the fire point, fusing the fire point information and monitoring data of the multiple satellites, generating time series data corresponding to the satellites, then acquiring geographical data and meteorological data of the target area, and respectively extracting geographical features corresponding to the geographical data, time series data and time series features corresponding to the meteorological data, splicing the geographical features and the time series features, obtaining a feature vector, identifying the fire point through a preset classification algorithm, judging whether the fire point monitored by the satellite is a real fire point, and if so, alarming, thereby improving the accuracy of fire point alarm.